我可以使用 group_map 或 group_walk 来迭代导出结果吗? [英] Can I use group_map or group_walk to iteratively export results?

问题描述

我想使用 group_walk() 或 group_map() 作为导入批量 .csv 文件的替代方法来迭代处理比较的主列表.

I want to iteratively process a master list of comparisons using group_walk() or group_map() as an alternative method to import batches of .csv files.

我想输入一个如下所示的数据集:

I would like to input a dataset that looks like this:

<头>

测试分析	Var1	Var2	频率
检测 1	否定	否定	19
检测 1	否定	位置	5
检测 1	位置	否定	8
检测 1	位置	位置	141
Assay2	否定	否定	25
Assay2	否定	位置	6
Assay2	位置	否定	17
Assay2	位置	位置	33
Assay3	否定	否定	99
Assay3	否定	位置	20
Assay3	位置	否定	5
Assay3	位置	位置	105

我想使用函数epi_analysis 并为每个测试分析(在本例中为Assay1、Assay2 和Assay3)导出一个csv.到目前为止，我有:

I want to use the function epi_analysis and export a csv for each Test Assay (in this example Assay1, Assay2, and Assay3). So far I have:

#Make export directory
check_create_dir <- function(the_dir) {
  if (!dir.exists(the_dir)) {
    dir.create(the_dir, recursive = TRUE) } #Creates a directory if it doesn't already exist
}

the_dir_ex <- "data_generated/epidata" #Name the new desired directory

check_create_dir(the_dir_ex) #Make the directory if it doesn't already exist

#Make function for the series of analyses
epi_analysis <- function(.x, the_dir){
  #Clean data
  dat2 <- .x  %>%
    select(c(Var1, Var2, Freq)) %>%
    pivot_wider(Var1, names_from = Var2, values_from = Freq) %>%
    remove_rownames %>% 
    column_to_rownames( var = "Var1") %>% 
    as.matrix() 
  
  #Run tests
  rval <- epi.tests(dat2, conf.level = 0.95)
  rkappa<-epi.kappa(dat2)
  gwet <- gwet.ac1.table(dat2)
  kappa2 <- kappa2.table(dat2)
  
  #Export results
  hd <- c('sensitivity', 'specificity', 'pfp', 'pfn', 'kappa', 'gwet', 'pabak')
  ests <- c(round(rval$elements$sensitivity$est, digits = 3), 
            round(rval$elements$specificity$est, digits = 3), 
            round(rval$element$pfp$est, digits = 3), 
            round(rval$element$pfn$est, digits = 3), 
            round(kappa2$coeff.val, digits = 3), 
            round(gwet$coeff.val, digits = 3), 
            round(rkappa$pabak$est, digits = 3))
  cis <- c(paste(round(rval$elements$sensitivity$lower, digits = 3), round(rval$elements$sensitivity$upper, digits = 3), sep = ","), 
           paste(round(rval$elements$specificity$lower, digits = 3), round(rval$elements$specificity$upper, digits = 3), sep = ","),
           paste(round(rval$element$pfp$lower, digits = 3), round(rval$element$pfp$upper, digits = 3), sep = ","),  
           paste(round(rval$element$pfn$lower, digits = 3), round(rval$element$pfn$upper, digits = 3), sep = ","), 
           kappa2$coeff.ci, 
           gwet$coeff.ci, 
           paste(round(rkappa$pabak$lower, digits = 3), round(rkappa$pabak$lower, digits = 3), sep = ","))
  
  df <- data.frame(hd, ests, cis)
  
  write.csv(df, 
            file = paste0(the_dir, "/", basename(.x$TestAssay)),
            na = "999.99", 
            row.names = FALSE)
  
}


#Use group_map or group_walk to iterate through the different assays in the dataset.

data <- read_csv("data_raw/EpiTest.csv") %>%
  group_by(TestAssay)%>%
  group_map(~ epi_analysis)

但是我的 Epidata 文件夹中没有 csv.欢迎提出任何建议/更正.

But there are no csvs in my epidata folder. Any suggestions/corrections welcomed.

推荐答案

您需要在 group_map 中调用您的函数.此外，该函数需要两个参数，因此也要传递 the_dir_ex.

You need to call your function in group_map. Also the function requires two arguments so pass the_dir_ex as well.

使用这个功能-

library(tidyverse)
library(epiR)
library(irrCAC)


epi_analysis <- function(.x, the_dir){
dat2 <- .x  %>%
  select(c(Var1, Var2, Freq)) %>%
  pivot_wider(Var1, names_from = Var2, values_from = Freq) %>%
  remove_rownames %>% 
  column_to_rownames( var = "Var1") %>% 
  as.matrix() 

#Run tests
rval <- epi.tests(dat2, conf.level = 0.95)
rkappa<-epi.kappa(dat2)
gwet <- gwet.ac1.table(dat2)
kappa2 <- kappa2.table(dat2)

#Export results
hd <- c('sensitivity', 'specificity', 'pfp', 'pfn', 'kappa', 'gwet', 'pabak')
ests <- c(round(rval$elements$sensitivity$est, digits = 3), 
          round(rval$elements$specificity$est, digits = 3), 
          round(rval$element$pfp$est, digits = 3), 
          round(rval$element$pfn$est, digits = 3), 
          round(kappa2$coeff.val, digits = 3), 
          round(gwet$coeff.val, digits = 3), 
          round(rkappa$pabak$est, digits = 3))
cis <- c(paste(round(rval$elements$sensitivity$lower, digits = 3), round(rval$elements$sensitivity$upper, digits = 3), sep = ","), 
         paste(round(rval$elements$specificity$lower, digits = 3), round(rval$elements$specificity$upper, digits = 3), sep = ","),
         paste(round(rval$element$pfp$lower, digits = 3), round(rval$element$pfp$upper, digits = 3), sep = ","),  
         paste(round(rval$element$pfn$lower, digits = 3), round(rval$element$pfn$upper, digits = 3), sep = ","), 
         kappa2$coeff.ci, 
         gwet$coeff.ci, 
         paste(round(rkappa$pabak$lower, digits = 3), round(rkappa$pabak$lower, digits = 3), sep = ","))

df <- data.frame(hd, ests, cis)

write.csv(df, 
          file = sprintf('%s/%s.csv', the_dir, .x$TestAssay[1]),
          na = "999.99", 
          row.names = FALSE)

}

并用 -

read_csv("data_raw/EpiTest.csv") %>%
  group_by(TestAssay)%>%
  group_map(~epi_analysis(., the_dir_ex), .keep = TRUE)

这篇关于我可以使用 group_map 或 group_walk 来迭代导出结果吗?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！